Multiple-input multiple-output (MIMO) radar has waveform diversity and large spatial degrees of freedom (DoFs), making it attractive for high-resolution sensing. Scaling MIMO radar to massive arrays can further improve sensing performance, but it also increases hardware cost, power consumption, and digital processing complexity. The microwave linear analog computer (MiLAC) can tackle these challenges by moving linear operations from the digital domain to the analog domain. MiLAC has shown promising benefits for communications in recent studies and this paper identifies its potential for radar sensing. Specifically, we consider both MiLAC-aided transmit beamforming and receiver-side two-dimensional discrete Fourier transform (2D-DFT)-based direction-of-arrival (DoA) estimation. For transmit beamforming, we formulate a weighted Cramer Rao bound (CRB) minimization problem under lossless and reciprocal MiLAC constraints and propose a penalty dual decomposition (PDD)-based iterative algorithm to address the non-convex problem. We further prove that MiLAC-aided and fully-digital beamforming achieve the same CRB. For receiver processing, we show that the 2D DFT can be implemented by a lossless reciprocal MiLAC, which enables analog-domain DoA estimation without digital optimization. Numerical results confirm the theoretical finding and show that the MiLAC-aided approach achieves the same CRB and DoA estimation performance as the fully-digital benchmark. Meanwhile, hardware cost and power consumption are reduced because only low-resolution DACs are required at the transmitter, while RF chains and ADCs are eliminated at the receiver. Moreover, performing the 2D DFT in the analog domain eliminates all digital DFT operations for DoA estimation.

翻译：多输入多输出(MIMO)雷达具有波形分集和大空间自由度(DoFs)，使其在高分辨率感知领域具有吸引力。将MIMO雷达扩展至大规模阵列可进一步提升感知性能，但同时也增加了硬件成本、功耗和数字处理复杂度。微波线性模拟计算机(MiLAC)通过将线性运算从数字域转移到模拟域，能够应对这些挑战。近期研究已表明MiLAC在通信领域的显著优势，本文则揭示了其在雷达感知中的潜在价值。具体而言，我们同时考虑MiLAC辅助的发射波束赋形以及基于二维离散傅里叶变换(2D-DFT)的接收端波达方向(DoA)估计。针对发射波束赋形，我们在无耗互易MiLAC约束下构建了加权克拉美罗界(CRB)最小化问题，并提出基于惩罚对偶分解(PDD)的迭代算法以处理该非凸问题。我们进一步证明MiLAC辅助波束赋形与全数字波束赋形可实现相同的CRB。在接收处理方面，我们证明2D DFT可通过无耗互易MiLAC实现，从而在模拟域完成DoA估计而无需数字优化。数值结果验证了理论发现，表明MiLAC辅助方法在CRB和DoA估计性能上均与全数字基准方案相当。同时，由于发射端仅需低分辨率数模转换器(DAC)，接收端去除了射频链和模数转换器(ADC)，硬件成本与功耗得以降低。此外，在模拟域执行2D DFT消除了DoA估计中所有的数字DFT运算。